Milvus is a high-performance, cloud-native open-source vector database built for large-scale vector similarity search in AI and GenAI applications. As an open-source vector database specifically built for GenAI applications, Milvus can handle billion-scale vector searches while maintaining minimal performance loss.
Milvus supports a wide range of in-memory and on-disk indexing/search algorithms, including IVF, HNSW, DiskANN, etc., all of which are deeply optimized. Milvus achieves a 30%-70% performance improvement compared to popular implementations like FAISS and HNSWLib.
Milvus adopts a distributed architecture with separated compute and storage, which can be horizontally scaled and adapted to different traffic patterns. Optimal performance is achieved by independently increasing query nodes to handle read-intensive workloads and data nodes to handle write-intensive workloads.
Based on a Kubernetes-based stateless microservices architecture, it can quickly recover from failures, ensuring high availability.
Milvus supports Binary, Float32, Float16, and BFloat16 vector types to meet the needs of different application scenarios.
Milvus adopts a microservices architecture, with the main components including:
Milvus supports four data consistency levels, including strong consistency and eventual consistency.
Milvus can be deployed as an embedded database, a standalone server, or a distributed cluster. At the same time, Milvus can be fully used in the cloud through Zilliz Cloud, which optimizes operating costs and vector search speed through four advanced features: logical clusters, separation of streaming and historical data, tiered storage, auto-scaling, and multi-tenant hot and cold data separation.
Milvus is well-suited for building RAG systems, capable of managing and searching large-scale vector collections, and handling large datasets containing billions of entries.
Personalized recommendation engines based on vector similarity.
Supports similarity search for multimedia content.
Text semantic search and document retrieval.
from pymilvus import MilvusClient
client = MilvusClient("milvus_demo.db")
client.create_collection(
collection_name="demo_collection",
dimension=768
)
Milvus provides integration support with mainstream AI frameworks:
Milvus is an open-source vector database designed to enhance embedding similarity search and support AI applications. It is a breakthrough tool that democratizes unstructured data search, ensuring a unified user experience across different deployment environments.
Milvus is designed from the ground up as a vector database, providing more intuitive APIs, a wider range of indexing methods and distance metric support, and SQL-like query potential, highlighting its advantages in managing and querying unstructured data.
As a vector database specifically designed for the AI era, Milvus has significant advantages in handling large-scale vector data and supporting complex AI applications. Its distributed architecture, high-performance algorithms, and cloud-native design make it an ideal choice for building modern AI applications, especially in areas such as RAG, recommendation systems, and similarity search.